{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/anaconda3/lib/python3.7/site-packages/dask/config.py:161: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(f.read()) or {}\n",
      "/anaconda3/lib/python3.7/site-packages/dask/dataframe/utils.py:13: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n",
      "/anaconda3/lib/python3.7/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  defaults = yaml.load(f)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "#import wfdb as wf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from biosppy.signals import ecg\n",
    "import glob\n",
    "from scipy import signal\n",
    "import pandas as pd\n",
    "from tensorflow.python.keras.layers import Dense, Convolution1D, Convolution2D,MaxPool1D, Flatten, Dropout\n",
    "from tensorflow.python.keras.layers import Input\n",
    "from tensorflow.python.keras.models import Model\n",
    "from tensorflow.python.keras.layers.normalization import BatchNormalization\n",
    "import tensorflow.python.keras\n",
    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.python.keras.utils.np_utils import to_categorical\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from collections import Counter\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os,glob\n",
    "import math\n",
    "import fnmatch\n",
    "import re\n",
    "import gc\n",
    "import shutil\n",
    "from joblib import Parallel, delayed\n",
    "#import matplotlib.pyplot as plt\n",
    "import scipy.signal as scipysi\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.multiclass import OutputCodeClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.neighbors import NearestCentroid\n",
    "from sklearn.neighbors import RadiusNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Bidirectional, GRU\n",
    "import shutil\n",
    "#import matplotlib as plt\n",
    "from tensorflow.python.keras.models import Model\n",
    "from tensorflow.python.keras.layers import Input, Dense, LSTM, multiply, concatenate, Activation, Masking, Reshape,CuDNNLSTM,GlobalMaxPooling1D, MaxPool2D,Flatten\n",
    "from tensorflow.python.keras.layers import Conv1D, Conv2D, BatchNormalization, GlobalAveragePooling1D, Permute, Dropout, GlobalAveragePooling2D,Concatenate\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import numpy as np\n",
    "from imblearn.over_sampling import SMOTE, ADASYN, SVMSMOTE\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from tensorflow.keras import initializers\n",
    "from tensorflow.keras import regularizers, constraints\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "from scipy import signal\n",
    "from scipy.fft import fftshift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12, 300, 32)       128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 6, 150, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 6, 150, 64)        51264     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 6, 150, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 3, 75, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 75, 128)        204928    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 3, 75, 128)        512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 1, 37, 128)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4736)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               606336    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 50)                6450      \n",
      "_________________________________________________________________\n",
      "main_output (Dense)          (None, 39754)             2027454   \n",
      "=================================================================\n",
      "Total params: 2,898,160\n",
      "Trainable params: 2,897,712\n",
      "Non-trainable params: 448\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "output_path='/Users/aring/Desktop/ECG-identification/Arnold-data/nn-input-all'\n",
    "model = tf.keras.models.load_model('/Users/aring/IdeaProjects/ECG-biometric/src/100k-data/identification-model-29-Jan-2021-14-11-08.h5')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X_loaded=np.loadtxt(output_path+'/testX.csv', delimiter=',')\n",
    "test_X_loaded=test_X_loaded.reshape(test_X_loaded.shape[0],12,300)\n",
    "test_X_reshaped=test_X_loaded.reshape((-1, 12, 300, 1))\n",
    "\n",
    "test_Y_loaded=np.loadtxt(output_path+'/testY.csv', delimiter=',')\n",
    "test_Y_loaded=test_Y_loaded[:,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_Y_loaded)\n",
    "test_y_encoded = le.transform(test_Y_loaded)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,11):\n",
    "    plt.plot( test_X_reshaped[1900][i,:]) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 300)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X_reshaped[1900][:,:,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "imgplot = pyplot.imshow(test_X_reshaped[1900][:,:,0], cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Actual', 1774.0)\n",
      "('Prediction', array([1774]))\n"
     ]
    }
   ],
   "source": [
    "print(('Actual',test_Y_loaded[1900]))\n",
    "prediction_proba=model.predict(test_X_reshaped[1900].reshape((-1, 12, 300, 1)))\n",
    "y_pred=np.argmax(prediction_proba,axis=1)\n",
    "print(('Prediction',y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "=================================================================\n",
      "Total params: 832\n",
      "Trainable params: 832\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "sub_model = Model(inputs=model.inputs, outputs=model.layers[1].output)\n",
    "sub_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import Model\n",
    "from matplotlib import pyplot\n",
    "from numpy import expand_dims\n",
    "import math\n",
    "\n",
    "def show_feature_maps(full_model,layer_num,nrows,ncols,number_of_filters,input_vector):\n",
    "    sub_model = Model(inputs=full_model.inputs, outputs=full_model.layers[layer_num].output)\n",
    "    sub_model.summary()\n",
    "    feature_maps = sub_model.predict(input_vector)\n",
    "    #square = int (math.sqrt(num_of_filters))+1\n",
    "    fig, axarr = plt.subplots(nrows=nrows, ncols=ncols)\n",
    "    ix=1\n",
    "    for row_no in range(nrows):\n",
    "        if ix>number_of_filters:\n",
    "            break\n",
    "        for col_no in range(ncols):\n",
    "            ax=axarr[row_no, col_no]\n",
    "            ax.set_xticks([])\n",
    "            ax.set_yticks([])\n",
    "            ax.imshow(feature_maps[ 0,:, :, ix-1], interpolation='nearest', aspect='auto', cmap='gray')\n",
    "            ix=ix+1\n",
    "    pyplot.show()\n",
    "    return feature_maps\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "=================================================================\n",
      "Total params: 832\n",
      "Trainable params: 832\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_maps=show_feature_maps(model,1,8,4,32,test_X_reshaped[1900].reshape((-1, 12, 300, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 12, 300, 32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_maps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,11):\n",
    "    plt.plot(feature_maps[ 0,i, :, 0]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12, 300, 32)       128       \n",
      "=================================================================\n",
      "Total params: 960\n",
      "Trainable params: 896\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ot1jZA+3wkytFBStkhmGYLqHtVacBWBEVYiYr8vF48V9u27YNn/rUp6DrOp566in8z//8j7JKg9Q2y4fdjI9UrjIs9rx+wmxaCXuTe1VZ8bvlhab2k7oglRsOh5HJZJQ99sDAAPL5vKVQxGgWu+tV1ciT/zWLeL2y8u5k1Wnx/OJPP9czNDSEtWvXIhKJYHp6Gq+//rqVE9ntnE6+Vi/7qvzHPuc72lp1Wmyf10gWcTuVLZLJJAYHB6HrOiqVCiYnJ9uWrdAJeSRNeLGrsB9XnWYYhjmfaIsPWexBSAWTSg0Gg4hEIohGo3U5ewm5iiwA/MM//APWr1+Pv/qrv8JPfvIT6++aptlWiCWftR1Uf44UMM18i9WW6XdVj+jFt9yqH0vls5X/Rj42+lxUEuI1UEx3MpnEmjVrMDo6avnfNE3Dxz/+cbzpTW/Ciy++iEceecS1bXTvKOcsLSrxAo1e5NGH6ndgceO5RR8gPS9ytXTTNOtyJBNvfetb8f73vx+vvPIKdu3a5bnSjVcVrnrG5BGVvN1i+1b9xCHTfbe7/4Zh4N3vfjcCgQCOHj1qzUEtBHY5luXfZfzanxUywzBMl9C2KAtZWYg1rAqFgqsPmLjxxhuxd+9efOlLX8Jvf/tbGIZhqRE3RUZKTPT9EqJ6VO0nq063KIHFiMkVRyGkLCiyQvTDFQoF1Go1xONxAMDBgwfrjjM8PAxN0/Cd73wHhw4d8nRuihhwg9SOaH8avahWZon+6sWM7ZbPLUbniPMT4fCZr4xKIff09AA4Y6tYLIZoNKrcrlnc7OM1Np1YyGdYpe5VKyGdIkA2bdqE97znPTh69ChOnjy54N87WR17HRX7GT2zQmYYhukS2v5CFns5XdeRTCbR19eHnp4e9PX1IZlMIhqNIhaLKfd/6qmnsGnTJtxyyy244YYbkEwmfZ2bqs36rdwr16ojn6mcg0NU36KyW4jeWu6pxegPsV5hKBTCkiVL6kYXIslkEuPj48hkMo61yvwi5yRQQcqT/NBk98XwddpFhah83tVqFaVSCcViUblCDwBWr16NeDyOvr4+DA4OwjCMjl8DtVcefdDfnO7HQsbv0nnIJ0/fMbEqNCHmuxG56qqrcOmllyKRSCAajVojwIVAXufgJ47bj41ZITMMw3QJLfuQ5ZldUqlixEMoFIKmaZYyjkQi0HUdpmkinU5jcnLS2vb06dP4xS9+gYMHD2LXrl3W3/v7+xGNRlEulxEInKlgXSgUUKlUlJmhCIryoDhj4JxKI4UmrhbyOjOuskMn1IZ4XNEnK6sgsnG1WoVhGFbWMZXf9+WXX8a+ffuQyWQsX6dXH78TNMKgNqo+F7Om2W23mJDKJBUnqk/x+VFF9MTjcVx33XVYsmQJJiYmFrrpyu8i4M/3vFCI0StiBBS1XfWdjkQi2LRpEw4fPoy9e/faKul2YzeKEj9vF6yQGYZhugTfClmMYhBn9p38UaRCRX+mYRioVquIRCLYtm0bVq1aBcMwMD4+jrvuuqvhGKT2ZBUWCoUstS1GHYi5H0Q13SqiMhVpRV07YWdTcTQixnGSmgPOKLYlS5bgxIn6VKzT09Po7+8HAMzNzVmjjnb29HbHI5W/mNEqTpA/m9ovqmWKsNA0zYrpFimXy1i2bBkCgQBSqZQVC94O5O+aXZvlVW/dYFMRcW5H9NMHg0HLp2yapnLNwYkTJ6yVpJlMpi2jOsLJvjRPo1oR2W77skJmGIbpEnwrZFEFttI7iLHFmqbhIx/5CG644Qb8+Mc/xlNPPaU8b7lcttSgmI2MejZaPSarZCeaUWikhtvdO6pmbu16bULMKSuqY+BMTg9VlEUqlcL09DSy2Wyd384NLyWKvMZl2uUHUG3r59jtgBQRRYCI/0jBqXjttdfw05/+FLt27cLY2Fjbq4V4sbvTSr6Fxum5kqvViBnh7CIYpqen8eSTT2LPnj2YnJxs64jUbUQhfu7luW0WVsgMwzBdQltW6rVCPB5HKBTC+Pg47rnnHnzrW99SbkeqNBAIoFKp1MXgktqiWXDTNK24XLeVd357ObGHbLffVXUsLzPk5Gujn6ZpIhKJIBwOI5FIYMWKFRgfH7f2+X//7/+hWq1ibm4O2WwWlUrFU6/frhwTfnxvi6XwSBGLmdjc2jI3N4eDBw/i9ddfRyaTaWtODi92cFrl1m2I30vy1bsp5PHxcezatQsHDx7E1NSU51wqftrk9nmnoqkIVsgMwzBdgt98yJNoQ+Xf/8WsMZsvpc62dacp+7JtPcHPbmfxZF9fL2SGYRimc7DLgmEYpkvgFzLDMEyX4CvKwql21tnPlbXW3Gabg8EgYrEYlixZgmKx2NY8APKMbaddNGYLNfXa3RbCqervecZUkz7klmwrP0Nitj2ZSCRSlw1wITOqtUhTtgUa7esnT7D408lW0WgUyWQS6XTaWgHZbXa1iw45205P9m1r2BuFWdFDSUuWc7mc7T70Mt62bRs+/vGP4+jRo/jud79ruz2Ft4lfBvF3tzR5cgC427Lv851QKIRIJIJSqVSXJL7dixY6geKL3dLEkfgseF0CK6aHpH1ogQwVAhBZu3YtisUicrkccrkcisXioiXgt0P14jBNsyXbimk/6b45LZ6ipdK0TBqAtSxa9WxedNFFeM973oNHH30Uw8PDANRFAkRaXVTkFgoqPg+qd44oSKvVqif7Nv1CVl1suVyuW+Mv5pawewHUajVkMhkUi0UcOHAA8XgcH/jAB2zrvMk3jF7QYnu8fgH8rhgTDS/aQFzZ1Sp2OR6c2kYvjXA4XBcrWSqVUK1W6zpEwzBw0UUXWTHI+XzeerF0wwtDvn5xtWWrKl+8PvF3p2eA8rCIMa9ihrJAIFCXs+Lw4cN1eZTpZzfFB3fqPsvCiDp/1XeDvqPRaNSyYzKZtLJCzs/P18XOv/TSSwDOVEmPRqMYHx9HKpWyVu1WKpWGTsDt+yzfd/n5cnpGxO+k2/fez71nHzLDMEyX4Fshi/XbxF6jVqs15DClv2mahsHBQfT09Fi5kKkSRyaTwcsvv4znn38ezz//PADgsssuw7/9279hamoK+/btw0svvYSZmRlrKCjmGKBesRlUylOusiD2huJ5ZH95O/y0KnXczL70u6ZpCAaDdVmxcrkcAoEAIpGIlUuarmWxVfJiqUg/IykxC5xhGA3uOFJ4wJkhuLh6shtGIJ3CTl3S6tpEImG5JijHiludxh07duAb3/gGBgYGcNttt+HRRx/11BYaocijWfEeqBSx+J2W579auX9+9mOFzDAM0yX4XanXsS4+mUxi48aNiMfjOH78OE6ePOn7GM34X8V9vdrCbnLorMJsS5SFn4xSsm9b3J98ayIDAwNWfmjyyYtKoIt5wTTNK/zuFAgETNme8rPiNLoR6yqSiqJJPXl0duGFF1q+01wuh1QqVTei6yR+nhnF9Tdl27PH8PTQiM+plwgUUVmn02nlJCF9b8Vrl/Ni+LGHSJu/C57sywqZYRimS+hItrdoNApd1xEMBlGtVlEsFl2rJ1x00UW47rrrUCwWUSqVrB4xn897CtESa+aRv9eLIhH9wF7iIVVhc53Az3FF1SdWYKBwLTk8aGpqytrGMAzrPon+ci/x43YjEr/tXwjk9vhpp1wj0olEIoHly5cjkUhgZGQEU1NTnp9Dao9f24n3nY6h+ikiRuO0A7mCjuhztxshaJqGWCwGXdet3ylMEzgTWTE/P1+3T09PjxW22dfXB8MwrCgt0zRRLBbrMj2qro+iZOQ1E52qgO7HzqyQGYZhugTfCln2VYrqjHqkfD6PfD6PQCAAwzAQi8UwMDAAwzAQCoVgGEadL+mll17C3r17sXfvXgDAli1b8Oyzz+JXv/oV/vZv/xaTk5Po6+tDOBxGPp+3eshSqVQXaeEUbREOh+tqdolVp+WeW/bJklrstOoTe1KVP9hOacnxrtT7x2Ix9PX1oVQq4fjx43X7BINB9Pb2WnagGGZSyjQ7rvLZOfnb2mWjBfDpKc8pVl2RfbIqf7zMX/zFXyAWi+GZZ57BiRMnPPuNW/EvyxFP1GY5qkCmnfZURSy4US6XkUqlbD//vd/7PXz1q19Ff38/brnlFtx///1Ip9PW55lMBgCg67qVu5rWJNCiE6qoQyNourf03NNiFLnttJ3X9Qz03NjdB6+5m1khMwzDdAlNKWR668u+GFodRohqmVbw6boOwzCQSCQQjUZRLBbR09OD+fl5DA0NYceOHTh9+jTe/OY31523UqlgaGjI8ueZpolsNotisWjFNIqQL4pinoFzdbxof3HG3G4ptur6qTeU1ZTbUk43VCvJvPqf5FEC+YUjkQiSyWTDasmhoSEsW7YM6XQaIyMjrj24HHftd9TQiuL1Ez3QCnRNpHJIVdHvAKyajaZp1qk14hOf+ATGx8exe/dulEqltuYREauLi7YQIwoWY2ShQj5nOBxGJBKxYpGpCr3Tc/fII48gm80iEongZz/7GYAzS9M//OEP49ixY9ZqXvGdIx6PKtKLoz9VZIyq7TRCpP3J7mJ0iFi1yG2E7hVWyAzDMF2Cb4Us+zhJcdYd9GzcZq1Wa/C7lUolK4pC5Atf+AK+9KUvwTAMXHnlldbf3/KWt2BmZgZjY2M4dOiQ+iLCYRiGAcMwoGmapYhLpRJyuRzm5uZcr4tq0InXpFKApFjFWWMvEQnN4sV/pdquVqshn89jfHwcExMTdffouuuuwwUXXIB0Oo0DBw549m+14kuXIwHoeHZ+TrvfOw3FZYv/FxWypmkwDMM26uLIkSN4+eWXcfLkSRQKhbatPpRXkMrY1aNb7GgXMdIim81afyP1mkgkLCVKK3vj8TjGxsYwNTVlKeOlS5fihRdewP79+/HRj37UOr6u64hGo9aIkEbQ5XLZitKyQ1TtNFKmvCXlchmVSsVThJgXG3CUBcMwzHlG0wrZCYpcCIVCiEajli8uFApZvQ+lJSRuv/123H777bjttttw4MAB/OIXv8Add9yBXbt2uZ6P1KwXH67KDwrA6gmpneR3Ev1FTukEFwo5EkD0K5ISoSrSgUBAGUP7ta99DeVyGQ888ABefPFFT+dtJj7Wrv12x+82aDRAio6eacrFoGlaw+jiueeew8jIiKWq2uFXpLY0Qztjjf2cRzVyE/9Gz6WcC4RyrwSDQSQSCSQSCZRKJUSjUVx55ZWYnJy0tg2FQlZGQzou3SfxvJTqU15VS++MfD6vtK+Yu8TLSNUt0sULrJAZhmG6hLav1BN7ilqthnK5XJdRjHoL8snJPdNdd91lKRLDMDAwMGCtLHPCi4JQxRaLn9FxSG3Sds36cTuB2G66HlLMpOorlQoikQjK5bLltyMMw8All1yCvXv3YnJysuHzVlBViyGbiGqzVRXRLtzUo2madbGl8mo6le89lUrh9OnTmJyctJ77xbo2YOFGHk5xzqrPRCUbCoUslRsInMsvnUqlkEqlsHHjRtxyyy145pln8J//+Z/WMWgf+knHqdVqCIfD1ne5VquhWCw2xAqLkRN21+TVfu16hlkhMwzDdAltV8ikKkhNUE9FvRj5e+3iM7/1rW/hwgsvxOc///mG1WXNIuZcEHsy2Z+pUm5eWGgFJMbLiquRyKYUf62aHd6wYQOOHTuGffv2YXp6um0+TrFtgPOqwm7xF7u1w+4aKOZUVHbE448/jtnZWUxMTCCbzfqKtW6HXWgUstg29mJb8XtJEU7hcLjBpq+99hp+8IMfIJFINByHRi7y/I5qvmchMu61CitkhmGYLqEj2d5EfyH1SHKhSE3TkM1mG3qsBx54AO973/uwevVqFAoFTE1NtaUgp2oVkzwDLCroxVYYhJ3aEaM/xNWC5EsWbS9iGAZee+01HDt2DPPz821VyG7qQx6ZLLSfsxnomlSrwWiuQ7Ths88+a8Xf+1Fj7bTBYthTnB8CGkejMuJqX1nlqiIl9u3bh+3bt2NwcBAzMzMIBoNWVjeys6i65dW0dE9afd7lrHqq61O9a7w+C6yQGYZhugS/FUMm0WIp9v/lrDFNc7CZHdm2nmjKvmxbT/Cz21k82dfXC5lhGIbpHOyyYBiG6RL4hcwwDNMl+IqyCHioLttMtimqbqHrOgqoB5DpAAAgAElEQVSFQsP69vMJs01Vp33uazuzK9uf8lGLmbEWohpKm5hq0ofs6bmVoz7cbBIKhdDb2wvDMJDNZh2rX5wHNGVboPVq9F5zOFM+ik7VvuswnuzbVNibkwGdwkDk0DIxXIXKsQDA4OAgQqEQ0um0FarS7gUMzSJee7OhLW7HdnrQ5KT4dE5KQC+mO4xGo3Wd21ve8hakUimMjIxYx5IXlVDIkbjwxK29qk7Yy35NfKE6NnHk1Gb5noupZ2dmZuq2pdBO+YVxHrw8OmZbp+dIXJIuviNCoVBDArJyuWyl2ZXLL4mpcLsUT/ZllwXDMEyX0NLCED9Kh3pCMVWknSqJRCIIBoPQdR1A68qzE7Qr8biImx01TbPSmdJy9Hw+bzuCIHV81VVX4dZbb0WlUsGf/umfWp+LIxSCFuGIAfB2SpjarFpgI+5/PqlFefm8+FP+HThzTxKJBEzzTEkxr8n+/y8h20wc5YllkOwIh8N45zvfiVwuh+eff77lUmmdwKvbxQ1WyAzDMF2C34UhnjcWl0lTD+h0rng8juXLlwMAJiYm6nzKinZYP5tRXu3qzVR0alJPHGEI52pQcaFQyFp+Gg6HUSqVHJeeG4ZhpewkPzKNYPymNFW1y24fcVsfvGCa5hV+djh7Pk8nEcvEiwmxnLa/6qqrsHnzZpw6dQpPPPGE5wlpp7kIN392M0mwPIxmm7Lt2WOb4jnod7fJUSqdJPuQqfyYyMqVK7F27VqMjY0hl8shlUpZybO6IZkSYTdyNk3Tk31ZITMMw3QJHVPIYk8uKy1KfkOFDoPBIJLJJJYuXYrTp0/j9OnTDceLxWIIh8OWShSjL+yShojJReySontR735oRSGr0oGKkSlOik3TNOsf+d9N00Qmk7FVbQMDAxgYGEA6nbYSPVFxR6dUhXKiqHbaUvZXiz8rlcqCKGRRqYnXJSoxKt8kX6tKrZGtxKQ5ot/UbTThpIrbqAxbVsg2nwFwbicVq6DtVXMbANDb24vBwUGEw2GcPn3aU/FiOmazdmrjaJoVMsMwzPmE7ygLVZo9+in3+nY9CSljXdfR19eH1atXY+PGjYjFYlbZb5lEImH5O4Ez6RCLxSLK5bKl5uizcDgMTdMsxUPtIr+gqP68+uBEmvGbekHe3+54cikhAFbxWJm+vj585CMfwdDQEH70ox9henoaAHDZZZdh2bJlOHz4MMbGxhzbRSk9SX2TklYpRBk54sIpZaH4u6gAO+EflJ9bGoHYjUICZ5Ooh8Nh6LpuqboVK1bgkksuQaVSwYMPPtjQ1t7eXiSTSesYVOBX/Cmfh0Y6dA7x+ZX/tXrdRKd8sKoIHPm8XtYY/O7v/i4uvvhiPPbYY3jhhReabkMzNDvn0cx+rJAZhmG6BN8KWVQuzcbiFotFa4a0XC5jy5Yt2LlzJ6699lokk0l87Wtfa9gnk8mgVCpZipfUDK000zQNwLmS32KSdlJzYpLxZpFXHQLnbLEQqwkpeoKWkZJ6slPI6XQa999/f93fvvvd72LLli244447rFV7TlAUhhdFDKiLyZJvVi6ECniLRW0V+V7R716Vi2maKJVKKJVKyOVyCIVCuPbaa/Fnf/Zn+NCHPoS77roLDz74YMN+uVzOimARn13xuRXVN5UyqtVqKJVKtveVrsmrf1Tli/Zrg1Yh24fDYYRCoTrfMT1jAJTJ/UdGRnDjjTdahWNHRkYwPT3dluIVbtj58N1sx3HIDMMw5zFNK2T6XTXLLG/nRDqdxtjYGNLptK1KMgwDuVxOGS1As9fiDDmV/RaVnRtiLKRb77eQqoIgdUlKja5JvPZEImGVwonH44hEInjjjTfqRgarVq3Ce97zHtx55511JdXj8bh1XcFg0Ipckf3zKmhOgKIJ5BFMN+Qiabe/tFqt4uTJk9i/fz/e9a534cILL7TdliI0KDpIfMbJJ18sFuuUNJ2DVKWqzX6uQ47KWKy4XXEuh54XXdetn+FwGLlcrmE0Ozo6isnJSWu7N954w/qs1Thku4ghcW6hWfy+D1khMwzDdAltL3Iq+ghJrbr1MEeOHMHtt9+On/3sZ3j44Yetv69duxYzMzNIp9O2+5IKJiXhd/ZZ5QeXfaCdnvEnxAgWuh6xfaJSFf2vpJYp+1UkEkG1Wq3zD8diMfzRH/0R1q5di82bNyvPTcej1X7kF5ftSb5PMYqF7gOp4VZspfLRd9r2YuyxF4LBIHp6erBixQqUSiU89dRTyu3IfpqmWYU2xeeUvi/i6IKeZ5XtxfYSzdhlMVa3ifdU9qfTP8paKHPppZdC0zS8/vrrmJiYqCsu2+p1OL2f5DkPvyMMv21jhcwwDNMltF0hE9QbijGodj1RKpXCr371K+v/kUgEb3/725FKpZSr9lR49fOoVu2JLJQaVqGK7/bj+xRVM8UbEx/60Ifwla98BY899phy31KphGq1Ck3TYJqmpbrJp0zHpVWAYhSLmOxehR811ooaaQXxefVy72u1GiYnJ7Fv3z6sWrXKdp6CFK64Uk8VZ+3HR9xsdNNi+Y3l85OdKcqCIkvK5bKlfOW2Xn755bjllluwf/9+HD16tG1tchtpiP5/oHEdRrttygqZYRimS2i7QiZFIEYtUK9CPaEYC6pSVVdffTUuuOACvPrqq4jH4yiVSm2JUZVVZ7tjCFtFpSS9toNyK4iz9CKVSgU9PT22cce04lHTNMuHKVYUIXUHoC5qgiJjnO6Pm9KTFYe8msyvf7cZRBUk5rFwolQqYXp6Gq+++ioOHz7suK1oJ6fjy+1QfS7+FKMwFlsBe0GMXJDbTNcSCoUa3gvPPfccHnjgARw/fhy6rrdlTYF4Ti9tlkc2nYAVMsMwTJfQdoVMPQcpKFJXlA+BZvBJic3PzzccY+XKlVi5ciXGxsbQ29uLbDbblioBflSEakVTp/HSPtHHSQoWgOXDpJhWmQ984AMwDMM2GkD0dcqqQaUgvOYCcYrD9OoT97JtqzQTazo+Po4f/ehH+NGPfgTgXL4L1cpDr7HYbvY8H1SwF0T7iKMFmo+Q0XUdzz77LA4dOtT2qiydiphoBlbIDMMwXYLffMiT6GB12v8FrDGbL6XOtnWnKfuybT3Bz25n8WRfXy9khmEYpnOwy4JhGKZL4BcywzBMl8AvZIZhmC7BV9hbwEOpelUiZztoEQMFiYthXKpji/j1fS9UMhWzhSKnHraRz+Xp2GLRU1VI3HnEVJOTegs2URIOhxvSN54nNGVbwNt7wc0O8oIYWiBCC5YIwzCsd4YcStjltvZk37bGIQcCAfT09CASiQA4szosn8/bVj2mlUsbNmzAN77xDVx//fW4/fbb8ZWvfKVhW5WxxdWAqrhZMbeDvAJMzia22Pl63QiHw4hGo9YXvlKpKGuyyQwMDGDHjh3YsmULnn76aezatctxe1Ucsvy5+JNwijNu4xel6Zl8u+fDLYZarLBNdrGrcr5y5UpomoZsNotSqYR8Pm/F1C72y8IprvysHToWJREIBBCLxay8x9Vq1bKP1Abr/5FIBLFYDIZhYMeOHUgmk7jnnnsc1yOIMeCqlZ2qVZA2tli0Z5ddFgzDMF2Cb4Usqks5V2+tVsPc3FzDPqFQyKpzp+u69fdqtYr5+XkcPXoUN910k/X3TZs2Ydu2bVbu07m5OWQymYYhSrO5DbysLFNtq1LY4vbtXkEkUqlUkMlkfGf6ymazePnll/Haa69hfHzcdXtZGdgpiPMVP/Yjl5qc4xtQr+zr6emx3EKUa2Ex8g77pdNtrNVqyGazyGazVh7tQCCASCRi60KjupvJZBLXXHMNVq5ciWw2q6xbSIgr/Egl072Tc3445SoRv9teXE9uo0Y/tmWFzDAM0yX4XalnUg5TlUq0W4cOnKlYkUgkLKd8JBKx/HFyflNd1/HFL34Ruq7jxIkT2LNnD8bHx1GtVpHNZjvq75Uzpal6R/G6RfV0dgKi5Uk9VY/rNBoQ84WI+6lyFF944YUwDMOqoJxOp1EoFLpK+drNCVSr1RdM07yiieOZ4nFknJSSWKtRbJcqZ0gkEsHAwAAqlQoKhQIKhYI1YS3m/ug2zirBpmx7dn9Tfvbk+2c3egyHw4jH41ZNRl3XUavV0Nvbi6VLlzbkXvngBz+IjRs3Ynh4GPv370c6nUYgEEA+n7feC82MVJ1yrojbeA1aUHgQPNmXFTLDMEyX4Fshu21D6pfy55bLZU891lVXXYVrrrkGwWAQhw8fxr59+6wIjampKdf9KZMcAKuKhZ/6aOJMejNhS4K/qGNhb8C5KtN0Ti81C6PRKHp7e6FpGoAzSi4SiSCVSuHUqVNe2mapbzmLmRfsqrTItvVg65YUss1nrueWR4PiiFC2hVwFQ8z7202jEAUtKWSHz6znx2+9S13XrbmnJUuWoK+vDwcPHmwY9dEohnJ3y1ESqnPKCpa2FX96eTZUqI7NCplhGOY8o+0KORAIwDAMRKNRK+6Q4mbL5XJD/KGK++67D5/4xCfw/e9/H//8z/+M/fv3t1VZyDOo9DdSn6oZVh/5bDu6MIQUg+y3t/Pdi6xfvx7bt29HIBDAk08+ibGxsWaaaosYkSDaThUPKuPx/rZVIXtRQCr/McW4qmze39+PZDKJcDiMdDqN2dlZT/eGjtvsc+4lLtxpv1Z9yF62C4VCMAzDihemdQpufP/738fNN9+M733ve7jlllusv19wwQUYHR113V+eY6Fn0st9ofsvKm9xPsCH3VkhMwzDnE80HYcsKkvRx1gul5HP5+t6vkgkgkQigSVLliCRSCCRSFgzq6lUCs8880zdOfbu3Yu1a9fijjvuwL59+wAAq1evRk9PjzWbWiqVMD8/j0Kh4OqjphU/mqZZvqZSqWTFi1INOa9Ks1O4KTbTNK3oCDFGNhwOIxKJWL/TP6r5Rhw7dgwDAwOYnZ2tU8e0iop6/Gq1inK5bP10ai/57mX1IUcXyNdxPiA/28C567O7hiuvvBJLlizB2NgY0um0r+epFXXsN4KkUzg9wxQlRUv5g8Egenp6YBgGTNO0ntv5+XkrpntkZASf+cxn8JnPfAYA8OUvfxlbtmzBD37wAzzxxBOWnzmXy0HTNOVI1s13LSpocUQs7kfHFOdw6Dq91F/0CitkhmGYLsGXDzkYDJpiLyKvhPGTE6K3txcAUCgUcMEFF+CSSy7BRRddhHK5jNtvv71u2yVLlsA0TStyo1qtWrGN5EOl80YiEct/TdvLargVRGUqX/vZ6thN+5BFdSH7Lb1GN+i6jlgsZtUuNAwDAwMD2Lp1KwYGBnDnnXc2xM/29fWhp6fHsivF0VYqFUuRExQrSiuugHP14ugfKYZmogpUq+LoGK3GIXvctqEtwvkd9127di1uvfVW5PN57N69G08//TRSqZTf5vpCjoUH6uNjfdyDluOQ5TkZOTrHyX6BwJk8OIFAALquY2BgAIFAAKlUCpOTk7Yr+kKhEFauXNkQ1UUr/VTVqWkuhkbMhDi6I3XsZ82DKhKH/l4ul9mHzDAMcz7hy4es6uXIVygjrx+XEZXD8PAwhoaGsHXrVitTHPHBD34QY2Nj2L9/v227xNhn6qlJDXvJiEbtFX2hdL0qtUFKsNP+uWZifmVFu337dnz+85/HTTfdhP/6r//Cd77znYZ90uk05ufnLV+aOAICYPn7RP80+dwLhQJKpZKntf5u9lLNWC9k/C4pOlKcYmx6MBikEZDtffnkJz+JT37yk3j00Ucb0kZ2CtEuzUZatBvxfssjUllJE6Zp1r0TJiYmAACf/exn8Qd/8AdIpVL47Gc/Wzcn8ra3vQ0HDhzAyMiIsh3i+gJd1xGJRKz4cJrrcktHKz4HTopZtYqPfvryQnjekmEYhukoLeVDdnrzi37QcDgMTdPqVHM2mwVwJuF0oVDAM888Uxdt8a//+q/QNA0//vGPcezYMcd2UEyhUy4NEVUcMvXmolKWYw87rdTEY7frPKdPn8Zjjz0GAPjNb36j3IZWOBGkAkQ/oGmeyWJG+WipfV5mmO1GUPJnC6WE7RCVL6kiMZJFHEGpshq++c1vRk9PD2ZmZjA9Pd3WiB3Rny0r72q1arsacqEQ2+R0btpO9LVS5BPNYdD+8Xgc99xzD+655x4Eg0HEYjHE43H09vYinU5jz549jm2ikQ0A5HI527zsbseg+0jvBvk62zmKY4XMMAzTJbS1YghQ35NTTCspWLkHCQaDiMfjGBoaQiwWwyuvvGJ99txzzyEWi2FmZqYuh7IdYpyg22yuqAZVSrlbS8OIOSy89MiapqGvrw9vectbsGLFijr7ytRqNcs3DJxbjSaOdFTndlK/sgr2OgJoZcVaq4jzJDQyoOsIBoNWrKuKVCqF3/zmNzh+/DgqlUpD5sBW2+V0z2u1Wp3du+WZJVTPghy9Iuaj6OnpwbJly7B+/Xrs2bMH09PTyGQyWLVqFa644gqcOHECp0+fxuTkJMrlsvJ7L456NU1T+rRVbRTbKR9PHD06bdssrJAZhmG6hLbX1BPX/gOo8w/JUIWRQqGARCJR99mvf/1rZLNZTE1N+cpvqjqPKheB+BPojD+oE8j5FJzaWS6XMT09jQMHDqBWqznWIwNg+fBIbYn+c/n88u9A/dp+eTs/9uwG29N1U9vFrG12z+PU1BSeeuopHD58GHNzc22tIOPFJk5RTd2AHJcs+8Lp//ReyOfzmJubw5IlS6DrOsbHxzEyMqKMqnAaFTvdMzvEdtqp5U7ACplhGKZL8JvtbRItVP79P8Aas/lS6mxbd5qyL9vWE/zsdhZP9vX1QmYYhmE6B7ssGIZhugR+ITMMw3QJ/EJmGIbpEnyFvTWbxtDNTx0MBq0E8pTE+nz1bZsdLnLqsL+qLQ1/o+RAYorB88jWU01O6nkqjyXixSaBQACRSAS6rlvJas5jmrIt0Pqz6+M8ALojLLIJPNm37Sv15JVwQH18KsV2UrwyxSjTOvONGzdidnYWk5OTDcelY1Fss9f20M9O5IpoJ/JLQV5FaLePmHtD3E9VASUej2NwcNCq6J3P51Eul5vKLNcp7GKYTdPs2Ey+nAvCS4XkQCCA/v5+DAwMIJVK4cSJ1pq3mLko0MEoCVXucDchQOIsEAggmUyiWq1ifn5euW0zdlMJGD/7N4En+7LLgmEYpktoSiHLuT/lTE/iqhnqHUWlplJjS5YswdKlS7FixQqsX78ezz77bF1GLbHnUlUxljO4yfkT7Ho+r72rXA1AVt5u6+S9IJ9fzCshZ/tS5d6Qs5PFYjFkMpk6e6VSKaRSKSQSCWs/MWeD35WKcp4L1XW0YoduHMkQlNuCMhkuJN28Ik+GMj5Srm3KbeP0falWqwiFQhgcHMS6deswNTWFQ4cOKbcV8604rVZ0WmWqOuZiwAqZYRimS2i66rSsEu18QpS9KR6PIxwO1/k5AVgVqmdnZzE7O4ujR49iw4YN2L59O06fPo3R0VGk0+mGSg2yCm62R/O6n3gOVb6GTkLXLebolVWyuB0Aq2pILBbDsmXLEIlEMDk5iZmZGQBAJpPx3Q67icPzRak5QfZVVYyxIxAIIJFIYOnSpb5GR07PjZstW8kRsljQiFnMB+JWq47mmvr7+5FMJl3zsKhGxYC9Ku7W55YVMsMwTJfgN5eFry5FrgpAf9M0DQAsvxKpNgBIJpPK2dRIJIJIJGIdi3xQVGnWbkacVI/s65QrI7ert+xE2JubEhKvz+2aotEotmzZgmQyiXw+j+PHj+P06dNe29i0ncQoBq/+ffG8Z7fraNVpeS7CKcqCnuPBwUGsWLECmUzG1sdJ1Y3FkY0YcihfvzwCVdHu5xYtVp32sa1tlkU3kskkNm/ejFgshtnZWZw4cQKzs7Oez9mMvVT3wu/zexauOs0wDHM+0XIcslOUgtgjiT4jsSqyruvYvHkzdu7ciZ07d+I3v/kNfv/3f195HlLIoVAIlUoF5XLZOi7V1KMZXU3TEA6H6/LZitWiZX+w36gCWXF3Mo7XKUKElBeNQNwWe2zatAk7d+5EKpXCvn37cPz48Zbb4QeV3dyOLcaxdxKvCooWMg0ODmLz5s1Yv349Dh06pFTIuq7DMAzrXonPIY3w6JzivZTzBnu1lRuLubhCjEcm5HkhJ66++mpceOGFOHDgAI4dO2Yblyzj9b6qFgfRv4Vaz8AKmWEYpkvwrZApllDGi9+NCAaD0HUdtVoNy5cvx7Jly3Do0CHccccd2L17t3IfivOluFnyHYvtCofD1vFpG7EitVutPYoWcULuMRdaacir8oBzVbe92H/Tpk3YunUrDh48iGq1Wjda6RTtiErpphlxevaWLFmCa665Bu9///vxyCOP4PHHH1duC5x7JkXVRVEzZBd6hsXRDo3oxJWDzdpR5ZdeSLuKq0qB+uvz0o6pqSlEo1HMz8+jWCx2rI2AuvqK/N3vBKyQGYZhugTfCln2T4qryLzOZIr13UZGRjA7O4sbbrgBt956K2677Ta89a1vxcTERN0+xWIR0WjUUsHAuXp9pGzJJ0d+ZFIbmqZZfmdKqiPjZwZWvF5R/bWzhpodou87FApZlajF6BNSVJQfRGTZsmV43/veh2KxiMcff9w1HtQPbv5hWV0scu6GlqCVZP39/bj44ovx4osvKrcjvzFdKz1/4n0kxShHopAqlvNqyPMffuik/9MN1YpSWlUaDAbrnudSqdTw/FYqFbz66qs4ePCg9bdQKIRoNGqtdyDVbVfpXoTOK7+76P7YVZ7vpN1YITMMw3QJvhWyKpJC7ElEPxj5fd1WMWUyGWiahmw2i+np6bqe8eKLL8bk5CSmp6frclvI6LqOeDyOUChk9Y7lctmzatV13YrKEHtZVcRCu2a8vSD7sahN5XIZgUAAuq4jFApZORUikYiVW2F6ehrZbLbueJlMBr/4xS/wwx/+ELt370Y6nbaOQfHcgUDAs3IWlTopNtF+op3aqcbbjd85AXrO6bmxW0nmNPKSVZfsI3bad6FzZ7gh+15l5GtVzenQ80vP09KlSzEwMIB169YhFArhoYceajiuYRiIxWLWeWlehI4tfv/F0bIclST+a/Y77eSj9zz6burMDMMwTNtpez5ksacn/w5wrgctFApW/l2Ru+++G3fffTc+9alP4dFHH8XExAR+/vOf47777nNdxw6c8Tl5iRgQYyHF+M5SqYRKpWL5skTfnuhX6iR+VY9pmtZscygUQrFYRCAQgGEY1jXJPPbYY3jyyScxPT0NXdeRSCRQKBQs1WKaZkM+a7F9ss+N1HCrme7k89hdb6cQo2y8nEeM+CHbqxCjCGgeA1DnW5aVsThKs2szbe+XTtnSTR07IY9oN2zYgOuvvx433HADDh06pFTI9IyLOV7oHopKWB7JmaaJcrlsjaRbpV05RlghMwzDdAkdVchi3B4pL8pfQVEJcu903333YevWrbjmmmvwpje9CZs2bcLLL7/cFgVGaobaRsg5DKhNfiIv2oF8Lj89LfnrAdiqVk3TkEql6jK9qaJFxFl8L/kn2m2jxYy48Ko6TdPE7Ows9u3bh8HBQRw5ckS5Hak1ccUo7S9HTsj+V7vnzy3PhVObO4XXlXB+2hAOh7Fy5Uq87W1vwxtvvKHchnzGNP8jjkhEu4uRE/Lq3Xa0VzxXK+8NVsgMwzBdQltyWdjNoFIvTtmuDMOw4jINw0Amk0EqlWo45o033ohsNovZ2dm2zibbZdeSr0dUi81miWoHfs4p+hpJGYfDYSSTSSxduhQAcOzYsYYRiXgOiowB6iuhyP70hYwyWSiauZ50Oo19+/Yhk8nYxiETcj5v1Tn9RFmcb3ZXjbbcqNVqyGQyOH78OA4cOGC7DY2+RcVLtpZXtIrn9+Kfb1YlNwsrZIZhmC7Bbz7kSXSwOu3/AtaYzZdSZ9u605R92bae4Ge3s3iyr68XMsMwDNM52GXBMAzTJfALmWEYpkvwFWURkGpn+cmtKlcLoJ+UoU0kGo2it7cXxWIRxWLRqgyyWNEOTijiRtteU69ZKDoiFAq5xlyeJ0w16UNu2bZeZs8pE6GXaJ4upCnbAt7sa5cJ0Gl72oZWnnYqB/IC4cm+TSWoBxpLGckJ4+tOEg4jFotB1/W6MkyBQAAzMzNIp9N122/cuBHXXXcdDh8+jOHhYbzxxhtIpVLWUsdOvVjkgHtVKJR83eI+nUq/SecQE5x7KQRAYW89PT2Ym5trSM4kLjdtR8jOAtDSxJHfZb2i3ekZB2D7/A0MDAA4k8BJTnKz2MhCSOTsM9WRSTn6rlNSHwqLdUo6RiXY8vk8AGDt2rWo1Wp47bXXlMenn+I9sruv8ndWpNVFHS54sm9T+ZCBRkMAsJSYTKVSQSaTQSgUgq7r1va0umb58uWYmJiwDLF//34MDAwgk8kgnU5b6807VbOOcLqJqpVpqpVVraB6yYtr81XXL+aUpVwJ5tk8FuVyGTMzM3VVva+44gqEw2GMjo4in88jm81aD77by6mT+Q/sOjlqF60+bAWnVW+qLyLFt9LvlBuB8u/K+XpPnz6NZDJp3YdA4ExebvGL7jOqybX9XlmsuHFaQUp5JMQsa3bQy3pwcBCf+tSnsG3bNjz++OPKF7IX0aTaR5WzRLVPp985MuxDZhiG6RL8xiE7bhwKhax8psCZoV2hUHDtZVatWoUbb7wRa9euxY9//GM899xzntqjykrmNBSRt5F9Wu1QD634kOWMUWI7yYcuEwqFYBiGtRqSfMa6riOXy2F6erpu+4svvhhr1qzB9PQ0xsbGkE6n66ogA/5VgdcVWM0oPtEGtVrtBdM0r/DVOHj3IdPwGnBexUX09/fjwx/+MAYGBnDgwAE899xzdXlC3M6lGgl4UbLic+/VH+vhe9GUbc8e35S/U17aJlfZJnurskECZ1yZ1WoVc3NzKBaLKBQKbc0y2CzytatGzV6fXVbIDMMwXUJbFTL5zMg3TL7CuDwAABI4SURBVD05RVLQP/KrBQIBxONxFItFZf03EfI9k4oRa8fJ2eVU7ZIrNYuqWKWEVGvZvdiqk1EWVNmD2uKlGsv69euxfft2GIaBxx9/HCdOOM8tOE3+0HlVv9vt2+ZJkqYVsmpUJP5U7GP55WlST8ytC6BhMho4MwKZm5vD5OQkgDPzJOLzJUZgeLVLO33JDrRdIRN2Iw2avBPfF2TjYrHYUO1m48aN6O/vx9zcHKampjA/P98wv9BJH3mzI0E/oztWyAzDMF2C7ygLOSOYrB4obtiOaDSKWCyGWCwGTdOQTCbR39+ParWK0dFRHDt2TLnf0qVLMTg4aFX2oMiNUqmEQqHQUB2D/KjUAwP1mejon13v7aSgxGsWlYGXiiWtQNftVV0Fg0FMTExg165diEajVqVkVbSCrusNGd3E6A6nkYeKbg2fo5qBwLn2q0ZJpnmuogSN+qh+Hj1XmqY1+OgPHTpU9//+/n7oum7ZkCrbUC5w1fMnVkwXI0DaVfuNrl2+3lbwMooUoyzcQt+AM3b44Ac/iB07duCNN97Af/zHfygjLWTsfOZye52ibkS7e7k2p5BKP7ZlhcwwDNMl+PYhU3C3k2IiJeqVnTt34pvf/CZWrFiBz33uc/jBD35gfbZ+/XoEAgEcPXrUrk2Wry8cDiMSiVg+qUqlgkKhgGw269pLUZQCtd0tTlHshckeZyMhmvYhiwpVjvN2sqmu65Z/mVScpmmoVCoYGxur21ZUyKFQCENDQ6hWq8jn89Z10+Ibu/PJqy6pzV4iE5pBsHPboiy8+gOd2vTRj34Uf/M3f4NLLrkEt912G77zne80bEeVjmW1K4486BmmxRPAuUoYtErVrg1er0GshUiICq7ZCJaz7fBkRHF+CaivTu723CQSCdRqNYRCIWSzWdd5Hzlu3stz6aaqVfh4ftiHzDAMcz7R1igLANaqMXG5Kaktp3MtWbIEf/iHf4ilS5fiqaeewi9/+UsAwLJly2AYBk6ePOl4Xk3TWvatUQyvqjqGky9VpJ1RFu3w861evRrvfve7sXHjRhw5cgR333133efhcNhaVSbODahWPokxo9QeL/fWK7JPXr7+crnc8Thkr9cRiUSwYcMGXHLJJUgmk3jwwQfrlqbHYjFlzUjxXGKNR0IabXlqszxak4/lkbZEWZDil32xdiqfRnW6rlvx9JFIBOVyue47v2PHDgQCAezevbtu/4GBAfT19Vm+6HK5jFwuh1KphGKxqPRRB4NBRCKRunkTsQ6l2/ySR5vIK/5YITMMw5xPdEwhk18XOOe/ERMDUc8VjUatXBUiDz/8MIaGhvDAAw/giSeewP79+z0l7/GickQlJqpeWQGqfH5uvWYn45ADgUCdj7xWq1l+RifWrVuHYDBo+eHf8Y534KabbsLY2BgeeOAB28gWglQM1S0rlUq+1IMYawrUx+K6RXJItFUhq+YBvBAMBq1IoXXr1qGnpwfPPPOMcpUeXTv57ukZ8pK9kBJwicijtmaR44Zb9SGLK+2cViD64W1vexv+5E/+BJ/+9Kexe/duXHPNNQ3bUNSLaZrW95reLWRjml+iyBj6rFgsun5/xIReTqgir8T7VK1WWSEzDMOcT7RcdRqon+0l1UYxuaQ4SSkYhgFd11GtVhGJRNDT04N0Oo3x8fG6Y548eRKvvvoqHnroIRw/fhyxWMyKwRXzLsi+KbuejHpJ8hFTW0m1i/Gd3YppmigUCgDOzVgbhoGenp66CJdCoYBgMIh0Og3TNDE8PGwd46//+q/x1a9+Fd/+9rdx5513Ym5uri6PgAqKwnCCojtoVCH64kRfquyDbofaaxbxvLI/1yk+lqohL1u2DJs2baq7L8CZiICVK1dienoa09PTjgpMXN1Kz6QYJ++GqKLFeGo7e3pdqegXuZq26rjiffdy7hdeeAH3338/1q5da/l85Vh/u1hm+p6LEVeVSgX5fL5ulOIFeYWv6qdbalavsEJmGIbpEtriQ/YbDRAIBKBpGuLxOJLJJLZs2YLly5fjnnvuqdtu69atCAaDmJ2dxdTUlOUTKpVKdf5IGVLD5Gd1q+CgWpXjdJ1OKqOTPmS5HeQ3o/PTikSaqS6VSg35ADRNw+c+9zmMjo7i8OHDGB0dxezsbDNNrkOMs6W2yXh91lSxsmfpWJSFuOpQvhav0TsXX3wxLrnkEoyOjuLAgQOYn5/321TH9snxtXIkjEgTo46OxiGrVnXSqkm3UemOHTuwZs0a7Nmzx3W+Q3VewH/+CbdtOmVfVsgMwzBdQlO5LGSol3PqMch/G4/HYZomwuGwFWExPDxsxR0Tn//85zE7O4tTp05hfn7e8hOpcjnQzKmoHMTtmvGTyfGdwDk1LldN8VIFoRnsencxXliMZyUfaDgcRrlcblDHAHDppZdicnISp06dQjqdbrnsFCm2dl77YvmTyf9XrVYRCJwrMyb6eMneuVyuwV8Yi8Vw1VVXYd++fchkMnj99dfbqpIB2H7PqG0ifuKqFwJVG708N3v27EGpVEJfXx8SiYTnnNN25/SDuP9CzHWwQmYYhukSfCtkeSUQ0LiGXNXr0SynWDk2kUigt7cXGzduxB//8R/je9/7Hg4fPgwA+Kd/+iesWLGi7lymaSISiSCfz1vnotloWa1Sz9Zsj0bnk/3G8u+d7DHdji322PK9ANQ1DkdGRrB06VIUi0XrPlF+kmZUbjtiYVs9TqeRffZ2M/TLly/HkSNHMDw8jFQq1dbsf17UWatqsJPII05x3saNgYEBLFmyBGNjY77UMeE0d+RmL9UIuZOwQmYYhukS/EZZTKLFUuz/y1ljmuZgMzuybT3RlH3Ztp7gZ7ezeLKvrxcywzAM0znYZcEwDNMl8AuZYRimS/AVZeF1RY7fmclQKISenh4YhoH5+fmmZlK7hYVYqedwDNdZazHb1WLlkGiBqSZ9yG29SFodSbHfYuSQYRh1uXRbzW2wgDRlW6D99pWO7Wk133mAJ/u2JbmQSDNfcsMwkEgkkMvlzuuXcTuwC8NxW3Qjpw0FoExrqus6BgcHUSwWkc/nUSgUUKlUXJeXLzQqO5im2faJI1r4IS6zd/ry08KQvr4+DA0NoVAo1JUXKxQK6O3tRTAYRKFQQLlcblg849XOcqhluxMCSXR0Uk6VBMntOhKJBC644AIYhoGRkRHMzMw0ff4uCLH0ZF92WTAMw3QJTSUXslEvTvvVFcYUe3tVmsH+/n709PQgm81a5Vi6bYhtZ4NOuSwCgUBdistareZYIojQdR3vete7sH37dhw5cgT333+/1/Z4/rt8T9qRnNyGjiUXouXn9Gx6KeHT19eH66+/HsuWLcPTTz+N559/3mt76n6Kv7st928m+bvH5F8dSS4klkUjZWw3CqGRXiQSgaZpmJubU24Xi8Ws+yMnBvNaws1ukUoH3y+cXIhhGOZ8oikfspdeRExCJCbdtmP16tVYtWoV8vk8jh49iuPHj3tqi9PSRz/KwW/PuNBKnVQbqThKJESJlOwIh8PIZDIYHR3F6Oio63nC4bClPOj4YhtUKkJeOi+3u1uwS9ZEf1eNNmh0JxYACIVCiEajSCaTeOihh+p8m/39/SiVSqhUKlZCdVraLypDP8V4Rbt6UXIdHKH4Rk7E5fR9peeYlpwvX74c27ZtQyKRwIsvvmj56nO5nOfz25233Qmx2gUrZIZhmC6h7UVOSb2JSb7d1HE0GsXAwADi8ThmZmYwMTFhu60YTQA0JthxUw5yj9lu31ErPmSxTbLqdOvRKSk/+d/o92KxiHQ6bSXFUZVi6u3tRSKRqCtGWywWLR+1XYkcSqlKbRULlrZaEsvGV920D1lWx37tSwUyqVhmPB7HypUr0dvbi927dyujg1atWgXTNFEsFhEIBCzlTIViVd8JivqQi2XSz3aXGhPOsSAJ6sXkX36uIxgMYvXq1VizZg0Mw8Ds7CxeeeUVT1FZ9Iw2Yzf5fUNt9/rOEWAfMsMwzPmEbx+yyg8n+6zsfJo046rret3PQqGAubk56LqOvr4+pULu7e1FLBaz/k/qrVwuW8qj4eLOJhYXZ89llbHYPjYZuS1e20ejEHGRQigUwhVXXIFPf/rT2LFjB37+85/ja1/7WsO+8/PzKJVKdUqAolrEQpFUrFZUHE72J7ymQRVTNKrUYSv3SWVX+e9O7ZQjWgqFAi699FJ84QtfwJ//+Z/j/e9/f932kUgE8XjcShkZiURQLpeteQDyK9M/MYIGOBdDToUWWsFpZLhQiCNRen7EUQAAq3gxjapFarUarr32WnzsYx/Dk08+iYcffriusKwTXhbniHMEom3oPSFGiNnNRbQDVsgMwzBdQlMKWfTD+ZlVp96GlAaVdNq8eTM++clP4p3vfCf++7//G9/85jeV5xVXPVEcIvlbKcl6KBSy/H2k+Cg5vp0/1O/1q3rKdszayhENqnOLMZ1u6qlareLgwYOYnp7G5s2bAUCpkO18y2IpdWoT2ZLugZs97WxlxwLEg9oqYT/nHBgYwOrVq1EqlfDDH/6w7rPt27fjzW9+MyYmJjA/P4/5+fm6Z49+iucjFUfPEa2e9HtdKiVMP7tlJChfKwBrjkP1TiFGRkZw77334te//jXGxsba0hbZty22S1bKCwErZIZhmC7Bt0IWe4pWe91qtWpFAKxevRpXXnklnnvuOeW28/PzqNVqVkIXsWwTAMv/JvqCxBltL0rO67V0g+oIBoPQdd0aKdj14NlsFv/yL/+CF198EdPT067HDYfDqFQqdaWKSNG4qQS7EZO4n9OoaqFsqZr/IJ+4E2SbZDKJdevW4a1vfSs2b96MvXv31m23adMmvPDCCxgZGUGxWERPTw80TasrjFoqleqiU2gEZzcyEle60j7yfbd7Hu1U80JD7RPbI/pmab6HvrtyXPjevXsRDofr5pio+Kx4XCdVq4pLptGe2wh1IWCFzDAM0yW0PdubGNcJeOtpZmZmsHfvXgQCATzwwAPKbUhZkOoVe0QAdX7OcrnsK4LCaRWRl9n5TiO2y4vSB874f2OxGEzThKZpqNVqMAxDuY+u64jFYpYKI38nqRQ75ShGsKj86eJPolv8mIQcl+wE2T6Xy2FkZATDw8O4+eabcdNNN+GrX/2qtd3u3buxatUq9PT04MUXX0QqlbJiwulcsh0oeoXsR/anNrrF8vsZ4XVCKcvnV42CVNETwLmYdk3ToOt6nT+XoluKxaIy21tPTw90Xbees0qlYsV4U1SLCEV6iXMxYuSV/Pw2YwcVXo/FCplhGKZL6JhCpt/deppQKIS5uTns3bsXx48fxwsvvOB6fBE6tqgc3Xojp5lo+ns3KTmxXWKv7rRtpVJBOp3GunXrcN111+H666/Hvn378MQTTzTsQ1EWpJDtRheBQMBarUYqhiItVNnRZJ9+M/dlIe6DGAni5XyRSAShUAjz8/N4+OGH8fd///fWZ1u3bsWqVatw4sQJzMzMIJ/PW8qXUI2yxPhxr+0U7WPXbtlX2imcRhhu5yX1L8YV9/f3493vfjc+9rGPoVqt4i//8i8bcrEEAgHk8/mGnNP0HJISpugkOescqe92RFDI0UTidfuxOytkhmGYLqHtCpkQ1Y5b3Gkul8P4+DhmZ2eV8bDy9m6/e2mT6u9+jrUYNKMYJyYm8Mgjj2B4eNhWxVD8Z7VarVMQ4uy+6AMk/73sf2u1vYvhnxfP7UXBk08+l8vh4MGDyOVyddnHVqxYgVgshkgkYo0kvPgjvapzP3HaC6WOAW8ROH7akMvlkM/nsXbtWlx55ZV46KGHGnJ503xRtVqFpmnWM0yfiSMtiusW/+Z2X5qNvBJ/+oUVMsMwTJfQdoVsF/tHPZM4g0oz+ul0Gi+99JL1d13Xcfnll2Nubg5TU1PIZrOe1607sZgKbDEIhULo7e3F6tWrkUgkbGO8SfmSr1PM2QugLlaU/r7QK5g6gR8FKaqlcrmM+fl5jI6ONjyXU1NTOHXqFObm5pDL5TyvDJV9wyqafW674Xmn773X6IVgMIhSqYR0Oo2TJ0/i2LFjyu3oeJTHWy4uK943MTez+JzL2PmCvdCqrVkhMwzDdAl+8yFPosPVac9z1pjNl1Jn27rTlH3Ztp7gZ7ezeLKvrxcywzAM0znYZcEwDNMl8AuZYRimS+AXMsMwTJfAL2SGYZgugV/IDMMwXQK/kBmGYboEfiEzDMN0CfxCZhiG6RL4hcwwDNMl/H8pX1VdiLx01QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_maps=show_feature_maps(model,2,8,4,32,test_X_reshaped[1900].reshape((-1, 12, 300, 1)))\n",
    "for i in range(0,11):\n",
    "    plt.plot(feature_maps[ 0,i, :, 0]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12, 300, 32)       128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 6, 150, 32)        0         \n",
      "=================================================================\n",
      "Total params: 960\n",
      "Trainable params: 896\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_maps=show_feature_maps(model,3,8,4,32,test_X_reshaped[1900].reshape((-1, 12, 300, 1)))\n",
    "for i in range(0,6):\n",
    "    plt.plot(feature_maps[ 0,i, :, 0]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12, 300, 32)       128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 6, 150, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 6, 150, 64)        51264     \n",
      "=================================================================\n",
      "Total params: 52,224\n",
      "Trainable params: 52,160\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_maps=show_feature_maps(model,4,8,8,64,test_X_reshaped[1900].reshape((-1, 12, 300, 1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,6):\n",
    "    plt.plot(feature_maps[ 0,i, :, 0]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs_cnn (InputLayer)      (None, 12, 300, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 12, 300, 32)       832       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12, 300, 32)       128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 6, 150, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 6, 150, 64)        51264     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 6, 150, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 3, 75, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 75, 128)        204928    \n",
      "=================================================================\n",
      "Total params: 257,408\n",
      "Trainable params: 257,216\n",
      "Non-trainable params: 192\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 128 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_maps=show_feature_maps(model,7,16,8,128,test_X_reshaped[1900].reshape((-1, 12, 300, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0lQyLyEr/8wHgFcCDnS5MoSbjDu+4pVKutgIGBI47lsLdynFrVKIkkEyI52wAviQiaTyh/6Yx5tbOlqUAM1FJ2Iw7nSVVOVE34waYiqVwlxsfMAUalSiJpKVwG2PuBS5YglqUubTRVSKVMn1zHPdAznubp8vOYla3NDjFxke6gs6dVBKJ7py0mcjCnSNlnMaLk7F03MUWjjuYO6nCrSQHFW6bCTbghN05mcqQrpQZmBOVDMR9cTKU49aoREkOKtw2U57yOipSId+mdI60md9VMhAsTsa1HTBUxq2OW0kOKtw2Uy6Ej0nAF25n1nZ3iHlU4rTYOantgEoCUeG2GWc6fEwCkM6SwZnfVZL1FidjKdxui52T6Zw3aEEdt5IgVLhtJuz0m4B0loxx6M/N2YCT897mQtyikkoFKk5zxy3ijy9T4VaSgwq3zUQUbpPyHfecqCSXTpFOCVOlmLUDVgcFN3Hc4B/tqlGJkhxUuG2mHHLCu48jWbK48xYnRYR8Nh2/qMSf5tPUcYMOU1AShwq3zTiF8NvdAUcyZOtk3OB1lsSuHdD1J7y3ctw66V1JGCrcNlOeDn/AFOCYNFlxGcjMf1vzuRg77pZRic6dVJKFCrfNRMy4y/4JBgOZ+edu92fT8evjDhy3RiWKMgsVbpuJ2A4YCHc+PV+g83GMSkI7bp07qSQLFW6bKRciRSXFwHGn5jvufC4T366Slo5bM24lWahw20x5OtLiZNl43SQD6fnCPRDLjDtYnGwh3Oq4lYShwm0zTrR2wJIv3P11HPdANh2/DThVxx2mq0SFW0kOKty2UnG9xbkIi5NF40Ul/an5kUg8u0oiOm5jOl+ToliACretRD2LGyhWvLezruOO4+JkaMedB8zMz0xRehwVbluJehY3UKxGJfW7SqbKLiZOrtQN67h1mIKSLFS4bSUQoQiOu1DxopI+5gv3QDaNWzGU3Plu3FqcCH3coJ0lSmJQ4baVsu+4Iwm393Zm62TcwdzJQilGwh3lkClQ4VYSgwq3rUSd8M6McOeovzgJMBWngcGhD5la7l01KlESggq3rbSxODntem+nVJoId5wWKEMfMqWOW0kWKty20o5w+xk3bnneY7EcGBzWcevcSSVhtBRuEXmWiNwhIrtF5H4ReedSFJZ4ytGjkhO+46461RpiOTA4bFeJzp1UEkYmxHMc4O+MMb8XkUFgp4j8yBjzQIdrSzZBxh1hy/u0I94ndRx3LKMSpwipTOsp9+q4lYTR0nEbYw4aY37vf34M2A2c3OnCEk+1qyS8455y/ck3daMS73f0dJwOmnJLrd021DhuFW4lGUTKuEVkBLgA2NGJYpQaqn3c4R33VJOoJLaOu9WuSahx3BqVKMkgtHCLyHLgW8C7jDGTdR7fJiKjIjI6Pj6+mDUmk+rOyfCO+3g5iEoaZ9yxEm63GM5xZ/pAUtY5bqficN1vrmP0ydFul6L0GKGEW0SyeKL9FWPMt+s9xxhzvTFmqzFm6/Dw8GLWmEza2IBzwvWFu047YCDcsToh0Cm1bgUEEPHHl9kl3J+/9/N8/aGv89UHv9rtUpQeI0xXiQA3ALuNMZ/sfEkK4IlQKgPpbOiXHHeaRCXZmDruMFEJ+OPL7IlK7hm/h3+/99/JpDL87snfUTEx2rGqWE8Yx30Z8JfAy0Tkbv/j1R2uS3EKkQ6YAjhWaizcmXSKXDoVL+F2Qi5OglXDFE6UT/C+n7+Pdfl1XHvRtRwtHuWhZx7qdllKDxGmq+QXxhgxxpxrjDnf/7htKYpLNOWpSDEJwIkgIanTVQLB0a5x6iqJ4rjtGV/28d9+nCeOP8HH/uhjvPyUlwOw46Cu5yuLh+6ctJWI8yYBph2okG4s3HGb9O6EXJwEa4T7x4/9mJsfuZmrz7ma5697Pmvza9myYgs7nlThVhYPFW5biTjhHbyFRzeVqRuVQAyn4Lil8I7bgqjkWOkYH/n1Rzh79dn8zXl/U73/4vUXs/OpnZQb/EJVlKiocNtKeTpSVFJ2KzgVQ0WyLaKSGAl3ZMfdXeH+70f/m6PFo3zg4g+QrVlUvnjDxUw709x3+L4uVqf0EircthJRuIM2v0oqA5X6wh1Pxx1lcbJ7UYkxhhv33MhZq87iecPPm/XYResvQhDNuZVFQ4XbVpxCtLO4y167mUllG0Yl/XHLuN2QfdzgtwN2z3Hfd/g+9hzZwxtPf+O8x1b0reCs1Wfxm4O/6UJlSi+iwm0r5elI291nHHeuYVSSj11UEsVxd3cDzk17bmIgM8Crt9TvlL14w8Xce/hepixpWVTijQq3rZSnI3WVVHdEphs77nwuE68JOG4xouM+ARGGIX/jwW/w2bs+u+ABysdKx7h97HZeveXVLA+m8czhkvWX4FQc7jp014K+l6JAuGNdlW4QOeP2d+alemxxMkrGjfEiphA/t5sfvpl/2vFPAJy64lRec+pr2i7ztkdvY9qZ5k2nv6nhcy5YdwGZVIYdB3dw2cmXtf29FAXUcdtLxHbAglPruBv3ccducTK04w5/tOv2/dv5yK8/wgs3vpDzhs/juh3X8dSJp9oqsXZR8uzVZzd83kBmgPOGz9OcW1kUVLhtJeIGnKqTTuea9nFPl90FRwNLRmTHTcvOkl2Hd/H3P/t7Tj/pdD75kk9y3Yuuw6k4fOhXH2rr57Lr8C4eOvIQbzz9jXjH+jTm4g0X8+AzDzJRnIj8fRSlFhVuGzHGc9xtLE5Kk4x7IJfGGCg6MTjwqOKCcSP0cQcDgxs77n2T+7jmJ9ewqn8V//aKf2NZdhmbhzbz7ue/m18e+CU37rkxcpk3Pdx8UbKWSzZcgsHw2yd/G/n7KEotKtw20sZZ3AVfjCWTq3usK8TshMDqoOCwOydbz5187y/eS8VU+NwrPseagTXV+//8jD/n0g2X8i+j/8Ljk4+HLnGiOMH3936fK7Zc0XBRspZz1pxDPpPnd0/+LvT3UJR6qHDbSBsT3quOO9MsKvHWoqficNCU6wt3VMfdICp5+MjD3Dt+L9vO3caWFVtmPSYifPSyj5KRDB/45QdCHcFarpT5h5/9A2W3zF+c+RehSsymsmwe2sz+Y/tDPV9RGqHCbSMLEO5Upq/xBpw4DVNw/D9DlNMBoWFUcssjt5BJZXjtqa+t+/j6Zeu59gXXctehu/jOI99p+q2MMfzzjn/m1wd/zQcv/SBnrDojXI3A2vxaDk0dCv18RamHCreNVKOS6MKdzmTB7YGoJKrjDqKSOo67XClz66O38pJNL+Gk/pMafok3PPsNXLj2Qj6181NNFxC//MCXuXHPjVx9ztX86Wl/Gq4+n7X5tYxP62g/ZWGocNtIdVBw9D7uVNOoJEbCHTjuKBtwoK7j3r5/O88UnmkpsiLCP178j0yUJvjsXZ+t+5w7Hr+DT4x+gldufiV/e+HfhquthuH8MM8UntGTApUFocJtI23MmyyUXTIp8aOSxhtwgHhswnHbXJyss6X8lodvYXhgmBdufGHLL3PGqjO46syr+OZD3+T+p++f9dj2/dt5z8/fw9mrz+a6F11HSqL/81mXXwegrltZECrcNuL4GXeErpLpsstANt10y3usJr07bS5OzukqOTx9mJ8/8XNe9+zXkUmF2yj89vPfzqr+VXzsNx+jYipMlaf46K8/yjU/uYaTl5/MZ1/2WQYinpUeMDzgDdLWnFtZCLrl3Uaqi5NR+rgr9AXC3ehY16z3dsfihEA34uJkpt+LVQpHZ939vT98D9e4XPmcK0N/66HcEO/e+m7e/4v384nRT3DnvjvZd2wfb33uW3nHBe+gL+wvkzqsza8FVLiVhaHCbSNV4Q7vuItll/5syt852SoqiUE7YFTHLQKD6+HYk9W7jDHc8sgtnD98/rwWwFa87tTX8a093+LLD3yZjcs2csOrbuCi9RdF+hr1CIRboxJlIahw20gb7YDTZZf+FlFJrBYnq447grsd3ACTB6o37zt8H49OPMqHL/1w5G8vIlz3ouu4be9tXHXmVQzmBiN/jXqs7FtJNpXlqan2zkZRFFDhtpNqxh1tcdLLuBs77v44tQNWHXfIqAQ84X5qZkHx5kduZiAzwKtGXtVWCZsGN7Ht3G1tvbYRIuK1BE6p41baRxcnbaTaVRJtAk5/NuUd62pc76yPOaRTQl8mFY8NONWukoiO+9jB6s07Hr+DlzzrJaG2oy8luglHWSgthVtEvigih0Rk11IUpFDTxx1hcdKpiUqg6RScWDju476wLVsb/jVDG6B0HIrHmChO8HThac5e1fio1W4xPDCswq0siDCO+z+Byztch1KLUwAkUkwwXXLpy6RnXtNsCk4chHtivxcV5VeFf83gBu86eZCxyTEARlaMLHppC0Udt7JQWgq3MWY78MwS1KIEBNNvWpzvXEvRqXhdI4HjbnBC4EAuzXQcxpdN7IMVmyL9DKrCfewgYxNjAIwMjSx6aQtlbX4tU84UJ7o4lV6JN4uWcYvINhEZFZHR8XFdeFkQEceWgbc42Z9J1UQlDTbhZGMyvmxivyfcUagV7skxMpLh5MGTF7+2BRK0BGpnidIuiybcxpjrjTFbjTFbh4eHF+vLJhOnEKmjBHzhzraOSgbiknG3JdzrvavvuDcNbiKbyi5+bQuk2sutnSVKm2hXiY2UpyI77umy60clgXA3Xpy0fuekU4TjT8GKZ0V7Xd9y6BuqZtw25tuguyeVhaPCbSMR500aY7x2wEwKgvM44txVMvmEd43quAEGN+BOHuDxycfZMhRtt+RSoeeVKAslTDvg14BfA2eIyH4RubrzZSWciBPegxmSfSGikv44ZNwT/oSYtoR7PQeO76dUKVnruPPZPMuzy1W4lbZpuXPSGHPVUhSi1BBxcTLYUDM7445xVLIQ4R7ayNgTj0JW2Dy0eXHrWkR0oIKyEDQqsZHIwu057oHaDTiNTgjMZeyfORkI91AbHSGD6xkrTwJ2tgIGDOeHtatEaRsVbhtxCtEmvFcdd7h2wEK5QqViFlxmx5jY5+2YjJDzVxncyFgmxWB2Oav6I2zeWWLW5ddpV4nSNircNlKejrzdHeZGJc1PCLQ6LmmnFTBgcD1j2Sxb8uuQKJt3lpjhgWHGp8ZDTZRXlLmocNtIeTqS2wwWG2c77hZncveqcA9tZCybYSS3cnFrWmTW5tfiGIcjhSPdLkWJISrcNhLVcfsZd5jFyYGs5XMnjVmQcJ/oH+JQJsOItBGzLCE6UEFZCCrctmGM3w4YIeOujUpSzTPufM5rJLK2l3v6iLcBqU3hHqt4Z5mPVOyNSUA34SgLQ4XbNtwymErksWUA/Zlwx7oC9naWTOzzru0K93GvI2WkVFisijqCCreyEFS4baONs7inZ3WVtN6AAxZHJRML2DUJjE2OIQZOOTG5iEUtPqsHViOICrfSFirctuH4TjFSO6Dfx50L08dt+eJkdfNNxHNKfMYmxtgoWfqO2d0jnU1lWdW/SoVbaQsVbtuoOu42dk5GikpsFe593mT3/Jq2Xj42OcZIdmjWCDNb0YEKSruocNvGUw9415NGQr9kuu6W98bHuoLNUcl+WHEypKL/1ayYCo9NPsaW/mGYOjwzcNhSdNu70i4q3Laxd7t3wNTJW0O/JIhK+jKp0O2A9i5Ott8KeGjqENPONCOD/uuP2x2XqONW2kWF2zb2bofNl0Im/LzJYtmlL5MilZIQx7p6j0+XLd2xN7G/7Xx778ReAEZOOt27Y9LuuGQ4P8wzhWcoN3ivFKURKtw2cfwQjO+GLS+O9LLq9BvwZjSmsk26SlKIwLSNjtste9n0AjpKAEbWPNe7w/Kce11+HaCbcJToqHDbxN7t3jWicE+XXa8VMCCdayjcIsJA1tJhCpMHANO+cE+Mkc/kWTscD+HWgQpKu6hw28Te7dC3AtafF+llhXKlml0DXmdJgynv4E/BsbEdcCHncOM57s1Dm5H8Kq8zxXLh1k04SruocNvE3u0wchmkW863mMWsqAQ84W7guMHiKTiL0MM9smLEi4sG11ufcet5JUq7qHDbwtHH4cjeyDEJQMGpeGPLAppEJeBPwbFSuP3t7m0MUHh88nEOnjjIaStP8+4Y3GC9417Zt5JsKqsDFZTIqHDbwt7O+HdKAAAIJElEQVSfe9d2hLvkeoOCA9JZcBtHJQO5jL1RSX415MJv9w+4YdcNZFNZrnzOld4dQ/YLt4h4vdw6UEGJiAq3Lezd7u0WHD4r8ktPlJzqxhqgaVcJQD6btrOrpM0e7oPHD/LdR77Ln532ZwznvQU/Bjd4UYmxeNIPsGHZBu4Zv4dSk/dLUeaiwm0DxnjCveWPIu8Y/OmDT3H/gUnO3VQzOKBFVDKQs7SrpM0e7i/u+iIAf3XOX83cObgByiegeGyxqusIb3ve29h3bB/X33t9t0tRYoQKtw08/Qc4diByTHL4eJFrb7qXM9cPcs1Lnz3zQDrbcAMOeMJt3SFTxngZd0THPT41zrcf/javf87r2bB8w8wDg/7nlscll518Ga879XXccN8N7Dmyp9vlKDEhlHCLyOUi8pCIPCIi7+10UYlj78+865Y/Dv0SYwzv/dZ9TBYcPv3m8+nLzFmcbHA6IARRiWXCXZiA0vHIwv2l+7+EYxyuPufq2Q8MxUO4Aa696FqG+ob48K8+jFux7H1RrKSlcItIGvi/wBXA2cBVInJ2pwtLFHu3e50Uq04N/ZKv/24fP979FO+5/EzOXD80+8EWjjtvY1QStAJG6Cg5UjjCN/d8kyu2XMEpQ6fMfjBw3Ja3BAKs7F/Jey56D/cdvo+v7P5Kt8tRYkAYx/0C4BFjzKPGmBLwdeANnS0rQVQqMPZzLyYJOZV87+ETfPR7D3DZc1bzv144Mv8JLfq4B3IZ+xx3Gz3c//XAfzHtTPPXz/vr+Q8OrveuMXDcAFdsuYIXb3ox/3r3v7L/2P5ul6NYjpgWq+4i8kbgcmPM2/zbfwlcbIx5R6PXbN261YyOjkYu5srrz6Uklh5+1CEEyFLmsKzmhCwL9Rqn4r1nm1fnydRbzJzY553rHcyfnINrDE7FYNNUxhQVMrjsT52MS7r1CwAnfZi+0jmcdPxtdR+/efIqXElxROye+B4wnoZrNkKfgUHLfq8q4VhmMty47e62XisiO40xoY4FDbNFr96/73lqLyLbgG0Ap5xyyrwXhGGY5bgmeX9jXdIczz2HrIQTLBBOWZVn9fIGJwim83B0X8NXO5UKx4v2tQOWpJ/BzOb6f+PqIIywOX8lA8uW1338+7m38OzCrkWssLOkgbdPTrF94Hi3S1HapJ/wk6sWQhjh3g/U/v91E3Bg7pOMMdcD14PnuNsp5vPbftXOyxSlAc/vdgGRuRB4a7eLUKwnTMb9O+A0EdkiIjngzcB3O1uWoiiK0oiWjtsY44jIO4Af4P1v7ovGmPs7XpmiKIpSl1DH0BljbgNu63AtiqIoSgh056SiKErMUOFWFEWJGSrciqIoMUOFW1EUJWaocCuKosSMllve2/qiIuPAY22+fA1weBHL6QRa4+KgNS4OcagR4lFnN2vcbIwZDvPEjgj3QhCR0bD79buF1rg4aI2LQxxqhHjUGYcaQaMSRVGU2KHCrSiKEjNsFO44DN/TGhcHrXFxiEONEI8641CjfRm3oiiK0hwbHbeiKIrSBGuE29aBxCLyRRE5JCK7au5bJSI/EpGH/etJXazvWSJyh4jsFpH7ReSdttXo19MvIr8VkXv8Oj/i379FRHb4dX7DPzq4q4hIWkTuEpFbbaxRRMZE5D4RuVtERv37bHu/V4rITSLyoP9381KbahSRM/yfX/AxKSLvsqnGZlgh3JYPJP5P4PI5970X+Ikx5jTgJ/7tbuEAf2eMOQu4BLjG/9nZVCNAEXiZMeY84HzgchG5BPg48Cm/ziPA1U2+xlLxTmB3zW0ba3ypMeb8mtY1297vzwC3G2POBM7D+3laU6Mx5iH/53c+3sSNKeBmm2psijGm6x/ApcAPam6/D3hft+uqqWcE2FVz+yFgg//5BuChbtdYU9t3gFdaXmMe+D1wMd5mh0y9vwddqm0T3j/YlwG34g1Ss63GMWDNnPuseb+BIWAv/hqajTXOqetPgF/aXOPcDyscN3AyUDskcb9/n62sM8YcBPCva7tcDwAiMgJcAOzAwhr9COJu4BDwI+APwFFjTDAA04b3/dPAtUAwtXo19tVogB+KyE5/1ivY9X6fCowD/+FHTl8QkWWW1VjLm4Gv+Z/bWuMsbBHuUAOJlcaIyHLgW8C7jDGT3a6nHsYY13j/Nd0EvAA4q97TlraqGUTktcAhY8zO2rvrPLXbfzcvM8ZciBctXiMiL+5yPXPJ4I3P/Jwx5gLgBJZGDv56xeuBG7tdSxRsEe5QA4kt4ikR2QDgXw91sxgRyeKJ9leMMd/277aqxlqMMUeBO/Ey+ZUiEkxi6vb7fhnwehEZA76OF5d8GrtqxBhzwL8ewstlX4Bd7/d+YL8xZod/+yY8IbepxoArgN8bY57yb9tY4zxsEe64DST+LvAW//O34OXKXUFEBLgB2G2M+WTNQ9bUCCAiwyKy0v98AHgF3oLVHcAb/ad1tU5jzPuMMZuMMSN4fwd/aoz5H1hUo4gsE5HB4HO8fHYXFr3fxpgngX0icoZ/18uBB7CoxhquYiYmATtrnE+3Q/aaBYJXA3vwcs/3d7uemrq+BhwEynhO4mq83PMnwMP+dVUX63sR3n/d7wXu9j9ebVONfp3nAnf5de4CPujffyrwW+ARvP+u9nX7Pffreglwq201+rXc43/cH/xbsfD9Ph8Y9d/vW4CTLKwxDzwNrKi5z6oaG33ozklFUZSYYUtUoiiKooREhVtRFCVmqHAriqLEDBVuRVGUmKHCrSiKEjNUuBVFUWKGCreiKErMUOFWFEWJGf8fFvFQucO/sq4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,3):\n",
    "    plt.plot(feature_maps[ 0,i, :, 2]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
